Speaker Identification Based on Robust AM-FM Features

Linear source-filter models have been widely used by researchers as a front-end for speaker identification systems. It uses the cepstral features derived from the power spectrum of the speech signal. But it is also well known that a significant part of the acoustic information cannot be modeled by the linear source-filter model, and thus, the need for nonlinear features becomes apparent. In this paper, an attempt is made to investigate the use of phase function in the analytic signal for deriving a representation of frequencies present in the speech signal. The main objective of the paper is to present a novel parameterization of speech that is based on the nonlinear AM-FM speaker model in the context of close-set speaker identification. The proposed features measure the amount of amplitude and frequency modulation and attempt to model aspects of the speaker related information that the commonly used linear source-filter model fails to capture. To evaluate the robustness of the proposed features for speaker identification, clean speech corpus from TIMIT database has been used and combined the speech signal with car noise and babble noise from the NOISEX-92 database. The proposed feature set provides significant improvements in the identification accuracy over the conventional method like MFCC under mismatched training and testing environments. The results show that better speaker identification rates are attainable under mismatched conditions especially at low signal-to-noise ratio (SNR).